t-dai-con/gpt-fine-tuned-v2
The t-dai-con/gpt-fine-tuned-v2 is a 7 billion parameter causal language model, fine-tuned from the h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b base model using H2O LLM Studio. This model is built on the Llama architecture and is designed for general text generation tasks, offering a 4096-token context length. It provides a readily deployable solution for applications requiring a fine-tuned Llama-based model.
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Model Overview
The t-dai-con/gpt-fine-tuned-v2 is a 7 billion parameter language model, fine-tuned from the h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b base model. This fine-tuning process was conducted using H2O LLM Studio, a platform designed for training large language models.
Key Characteristics
- Base Architecture: Utilizes the Llama model architecture.
- Parameter Count: 7 billion parameters, offering a balance between performance and computational requirements.
- Context Length: Supports a context window of 4096 tokens.
- Training Framework: Fine-tuned with H2O LLM Studio, indicating a structured approach to model development.
Usage and Deployment
This model is designed for straightforward integration with the transformers library, supporting GPU-accelerated inference. It includes examples for direct pipeline usage and custom pipeline construction, allowing for flexible deployment. The model also supports quantization (8-bit or 4-bit) and sharding across multiple GPUs for optimized resource utilization.
Considerations
As with all large language models, users should be aware of potential biases and limitations inherent in models trained on diverse internet data. Critical evaluation of generated content is recommended, and responsible, ethical use is encouraged.